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Benefiting from cloud computing, today's early-stage quantum computers can be remotely accessed via the cloud services, known as Quantum-as-a-Service (QaaS). However, it poses a high risk of data leakage in quantum machine learning (QML). To run a QML model with QaaS, users need to locally compile their quantum circuits including the subcircuit of data encoding first and then send the compiled circuit to the QaaS provider for execution. If the QaaS provider is untrustworthy, the subcircuit to encode the raw data can be easily stolen. Therefore, we propose a co-design framework for preserving the data security of QML with the QaaS paradigm, namely PristiQ. By introducing an encryption subcircuit with extra secure qubits associated with a user-defined security key, the security of data can be greatly enhanced. And an automatic search algorithm is proposed to optimize the model to maintain its performance on the encrypted quantum data. Experimental results on simulation and the actual IBM quantum computer both prove the ability of PristiQ to provide high security for the quantum data while maintaining the model performance in QML.

Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) of circuits and components within automotive systems. The Analog and Mixed-Signal (AMS) circuits prevalent in these systems are more vulnerable to faults induced by parametric perturbations, noise, environmental stress, and other factors, in comparison to their digital counterparts. However, their continuous signal characteristics present an opportunity for early anomaly detection, enabling the implementation of safety mechanisms to prevent system failure. To address this need, we propose a novel framework based on unsupervised machine learning for early anomaly detection in AMS circuits. The proposed approach involves injecting anomalies at various circuit locations and individual components to create a diverse and comprehensive anomaly dataset, followed by the extraction of features from the observed circuit signals. Subsequently, we employ clustering algorithms to facilitate anomaly detection. Finally, we propose a time series framework to enhance and expedite anomaly detection performance. Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios. By monitoring the system behavior under these anomalous conditions, we capture the propagation of anomalies and their effects at different abstraction levels, thereby potentially paving the way for the implementation of reliable safety mechanisms to ensure the FuSa of automotive SoCs. Our experimental findings indicate that our approach achieves 100% anomaly detection accuracy and significantly optimizes the associated latency by 5X, underscoring the effectiveness of our devised solution., Comment: 12 pages, 12 figures

Quantum computing offers significant acceleration capabilities over its classical counterpart in various application domains. Consequently, there has been substantial focus on improving quantum computing capabilities. However, to date, the security implications of these quantum computing platforms have been largely overlooked. With the emergence of cloud-based quantum computing services, it is critical to investigate the extension of classical computer security threats to the realm of quantum computing. In this study, we investigated timing-based side-channel vulnerabilities within IBM's cloud-based quantum service. The proposed attack effectively subverts the confidentiality of the executed quantum algorithm, using a more realistic threat model compared to existing approaches. Our experimental results, conducted using IBM's quantum cloud service, demonstrate that with just 10 measurements, it is possible to identify the underlying quantum computer that executed the circuit. Moreover, when evaluated using the popular Grover circuit, we showcase the ability to leak the quantum oracle with a mere 500 measurements. These findings underline the pressing need to address timing-based vulnerabilities in quantum computing platforms and advocate for enhanced security measures to safeguard sensitive quantum algorithms and data., Comment: 10 pages, 9 figures, submitted to IEEE HOST 2024